2026-04-17T17:19:09Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/84082025-10-24T10:29:55Zcom_2072_378070com_2072_378040col_2072_378092

Validation of an autonomous artificial intelligence–based diagnostic system for holistic maculopathy screening in a routine occupational health checkup context Font, Octavi Torrents‑Barrena, Jordina Royo, Dídac Bures, Anniken Salinas, Cecilia Zapata Victori, Miguel Angel Banderas García, Sandra Zarranz-Ventura, Javier Institut Català de la Salut [Font O, Royo D] Optretina Image Reading Team, Barcelona, Spain. [Torrents-Barrena J] BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain. [Banderas García S] Facultat de Cirurgia i Ciències Morfològiques, Universitat Autònoma de Barcelona, Bellaterra, Spain. Servei d’Oftalmologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. [Zarranz-Ventura J] Institut Clinic of Ophthalmology (ICOF), Hospital Clinic, Barcelona, Spain. Institut d’Investigacions Biomediques August Pi I Sunyer (IDIBAPS), Barcelona, Spain. [Bures A, Salinas C] Optretina Image Reading Team, Barcelona, Spain. Instituto de Microcirugía Ocular (IMO), Barcelona, Spain. [Zapata MA] Optretina Image Reading Team, Barcelona, Spain. Servei d’Oftalmologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain Vall d'Hebron Barcelona Hospital Campus Retina - Malalties - Diagnòstic Intel·ligència artificial INFORMATION SCIENCE::Information Science::Computing Methodologies::Algorithms::Artificial Intelligence DISEASES::Eye Diseases::Retinal Diseases Other subheadings::Other subheadings::/diagnosis CIENCIA DE LA INFORMACIÓN::Ciencias de la información::metodologías computacionales::algoritmos::inteligencia artificial ENFERMEDADES::oftalmopatías::enfermedades de la retina Otros calificadores::Otros calificadores::/diagnóstico Diabetic retinopathy; Retinography; Screening Retinopatía diabética; Retinografía; Cribado Retinopatia diabètica; Retinografia; Cribatge Purpose This study aims to evaluate the ability of an autonomous artificial intelligence (AI) system for detection of the most common central retinal pathologies in fundus photography. Methods Retrospective diagnostic test evaluation on a raw dataset of 5918 images (2839 individuals) evaluated with non-mydriatic cameras during routine occupational health checkups. Three camera models were employed: Optomed Aurora (field of view — FOV 50º, 88% of the dataset), ZEISS VISUSCOUT 100 (FOV 40º, 9%), and Optomed SmartScope M5 (FOV 40º, 3%). Image acquisition took 2 min per patient. Ground truth for each image of the dataset was determined by 2 masked retina specialists, and disagreements were resolved by a 3rd retina specialist. The specific pathologies considered for evaluation were “diabetic retinopathy” (DR), “Age-related macular degeneration” (AMD), “glaucomatous optic neuropathy” (GON), and “Nevus.” Images with maculopathy signs that did not match the described taxonomy were classified as “Other.” Results The combination of algorithms to detect any abnormalities had an area under the curve (AUC) of 0.963 with a sensitivity of 92.9% and a specificity of 86.8%. The algorithms individually obtained are as follows: AMD AUC 0.980 (sensitivity 93.8%; specificity 95.7%), DR AUC 0.950 (sensitivity 81.1%; specificity 94.8%), GON AUC 0.889 (sensitivity 53.6% specificity 95.7%), Nevus AUC 0.931 (sensitivity 86.7%; specificity 90.7%). Conclusion Our holistic AI approach reaches high diagnostic accuracy at simultaneous detection of DR, AMD, and Nevus. The integration of pathology-specific algorithms permits higher sensitivities with minimal impact on its specificity. It also reduces the risk of missing incidental findings. Deep learning may facilitate wider screenings of eye diseases. Open Access Funding provided by Universitat Autonoma de Barcelona. 2022-11-07T11:10:11Z 2022-11-07T11:10:11Z 2022-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Font O, Torrents-Barrena J, Royo D, Barrena-García S, Zarranz-Ventura J, Bures A, et al. Validation of an autonomous artificial intelligence–based diagnostic system for holistic maculopathy screening in a routine occupational health checkup context. Graefes Arch Clin Exp Ophthalmol. 2022 Oct;260:3255–65. 1435-702X https://hdl.handle.net/11351/8408 10.1007/s00417-022-05653-2 35567610 000795627500001 http://hdl.handle.net/11351/8408 eng Graefe's Archive for Clinical and Experimental Ophthalmology;260 https://doi.org/10.1007/s00417-022-05653-2 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf Springer Scientia